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Record W4384500680 · doi:10.1109/jlt.2023.3292455

Guest Editorial Guided Lightwaves for Sensors & Measurement Systems: Advanced Techniques and Applications

2023· editorial· en· W4384500680 on OpenAlexaff
Gaozhi Xiao, Tuan Guo, Fei Xu, Francesco Chiavaioli

Bibliographic record

VenueJournal of Lightwave Technology · 2023
Typeeditorial
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsInstrumentation (computer programming)Electrical engineeringComputer sciencePhotonicsTelecommunicationsElectronic engineeringPhysicsEngineeringOptoelectronicsOperating system

Abstract

fetched live from OpenAlex

This IEEE/OSA Journal of Lightwave Technology special issue on “GuidedLightwavesforSensors&MeasurementSystems:AdvancedTechniquesandApplications”is organized by the Photonic Technology in Instrumentation and Measurement Technical Committee (TC-42) of IEEE Instrumentation and Measurement Society. It is the 4thspecial issue on the same topic following the previous three successful ones (2017, 2019 and 2021).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.023
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.001
Science and technology studies0.0020.001
Scholarly communication0.0050.003
Open science0.0020.001
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0230.019

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.276
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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